Name
Abstract | ||
|---|---|---|
A key challenge to most conventional clustering algorithms in handling many real world problems is that, data points in different clusters are often correlated with different subsets of features. To address this problem, subspace clustering has attracted increasing attention in recent years. In practical data mining applications, data points may arrive in continuous streams with chunks of samples being collected at different time points. In addition, huge amounts of data often cannot be kept in the main memory due to memory restriction. Accordingly, a range of evolving clustering algorithms has been proposed, however, traditional evolving clustering methods cannot be effectively applied to large-scale high dimensional data and data streams. In this study, we extend the online learning strategy and scalable clustering technique to soft subspace clustering to form evolving soft subspace clustering. We propose two online soft subspace clustering algorithms, OFWSC and OEWSC, and two streaming soft subspace clustering algorithms, SSSC_F and SSSC_E. The proposed evolving soft subspace clustering leverages on the effectiveness of online learning scheme and scalable clustering methods for streaming data by revealing the important local subspace characteristics of high dimensional data. Substantial experimental results on both artificial and real-world datasets demonstrate that our proposed methods are generally effective in evolving clustering and achieve superior performance over existing soft subspace clustering techniques. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.asoc.2013.03.002 | Appl. Soft Comput. |
Keywords | Field | DocType |
clustering algorithm,conventional clustering algorithm,clustering method,subspace clustering,data point,scalable clustering method,scalable clustering technique,soft subspace clustering,soft subspace,data stream,data stream clustering | Fuzzy clustering,Data mining,Canopy clustering algorithm,Clustering high-dimensional data,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Constrained clustering,Artificial intelligence,Cluster analysis,Machine learning | Journal |
Volume | ISSN | Citations |
14 | 1568-4946 | 8 |
PageRank | References | Authors |
0.44 | 28 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin Zhu | 1 | 13 | 1.18 |
| Longbing Cao | 2 | 2212 | 185.04 |
| Jie Yang | 3 | 1392 | 157.55 |
| Jingsheng Lei | 4 | 691 | 69.87 |